According to the American Heart Association, coronary artery disease (CAD) is a leading cause of death in the western world. The current diagnostic standard for CAD is conventional invasive angiography (ICA), which involves a considerable amount of risk and cost. New generations of cardiac computed tomography (CT) scanners enable the acquisition of Coronary CT Angiography (CCTA) images with unprecedented quality. Coronary stenoses can be detected with high sensitivity in CCTA which allows that method to be used as a gatekeeper to invasive diagnostic and surgical procedures such as ICA.
Methods for the automatic detection of coronary stenoses in CCTA have been proposed for clinical trials. Recently, CCTA has also been proposed for the simulation of pressure distributions along coronary stenoses and for the computation of the so-called fractional flow reserve (FFR) which is indicative for ischemia-causing lesions. Both an automatic detection of coronary stenoses as well as the simulation of their hemodynamic relevance (i.e. the simulation-based detection of pressure drops within the coronary vessels) rely on accurate segmentation of the coronary lumen in the image data provided. This is a challenging task as coronary vessels are comparatively small (extending to only a few voxels in image data in their distal parts) whereas CCTA image volumes are of varying quality (in particular with relation to noise, artifacts, contrast homogenity etc.). Accurate segmentation is further complicated as the contrast of the vessel lumen is only slightly higher than that of non-calcified plaques but lower than that of calcified plaques. Therefore, non-calcified plaques appear optically very similar to the background of the vessel, in particular in a contrast-enhanced image acquisition process. On the other hand, calcified plaques appear to be part of the lumen of the vessel in such contrast-enhanced image acquisition processes as they show about the same appearance as the contrast agent in the image data. Therefore, a distinction between calcified plaque and lumen of the blood vessel through which blood can flow is very difficult to make.
This explanation shows how important an accurate segmentation of structures can be in the context of the advancement of methodologies of automatic evaluation of illnesses. While numerous segmentation methods for structures in image data do exist already, there still remains the need for particularly accurate segmentation methods and algorithms.